While testing research or a hypothesis, there are chances that people can commit mistakes. As scholars have to go through rigorous calculations followed by deep analysis of their data, understanding of some common statistical errors that they make is extremely important. With every analysis, there is a risk of making such types of blunders, but the amount of risk is in your control. Sometimes those mistakes might not make a huge difference, but there are instances in the past where statistical errors lead to the rejection of the hypothesis. If you are interested in knowing about those easy-to-correct errors, then read the article.
1.) Data Visualization Errors
One of the best ways to communicate information is a visual representation. It is the best way to make the data engaging and understanding. But, you commit certain common blunders while doing so. While using pie charts, you must check the sum of the angles to be always 100%. Also, if the data is large, then don’t go for pie charts. In such cases, using another type of chart will be a better option. Coming on the bar graphs, these are most suitable for the categorical data, but you must consider the following points when examining any bar graph: It shall have a right scale; always try to choose your scale based on the average difference between the numbers given in the data.
2.) Zero Margin of Error
The marginal error is a statistic that tells the amount of random sampling error in a survey data. There is always a room for faults to occur during calculation. Claiming that the marginal error is zero will make other raise their eyebrows as it can only occur in ideal conditions which are seldom. If you nullify this error, you are inviting trouble for yourself.
3.) Botched Numbers
It is a common tendency of humans to believe every data that comes before them. You must check that there are no botched numbers in the samples. To do so, you should ensure that everything adds up to what is given. Always double-check every calculation and get it reviewed by any third person.
4.) Taking Non-Random Samples
If you are going for a survey or research work and you are planning to take non-random samples, then this is the biggest error you can commit. This might kill the purpose of your work. Non-Random samples are often biased, and any study based on such data represents the information of a particular strata of society. It is crucial to ensure that every research or information gathering should be based on the random sample.
5.) Correlation vs. Causation
Most of the students get confused between correlation and causation. Theoretically, the difference between the two can easily be identified. But in daily practice, it remains perplexing for students to establish a difference between cause and effect, and establishing correlation. For those who mostly face this problem, a brief difference is given below:-
Correlation is a number used in statistics that describes the extent & direction of a relationship between two or more variables. If it is positive, then the relationship is directly proportional, and in case of negative correlation, two variables are inversely related.
Causation means that how the occurrence of one event can cause a change in the other, i.e., a causal relationship exists between the two events.
It’s time for you to keep away any ‘oops’ moment by avoiding the mistakes as mentioned above. People often commit errors in statistical calculations, but you can identify them by being a skeptic.
Hope you liked reading this write-up.
If writing lengthy assignments is troubling you, then why add more to your sufferings. Hire us to draft your academic documents. We, at Assignment Desk, have an experienced team of writers and experts who provide the best online Statistics assignment help to scholars at reasonable prices.
1.) Data Visualization Errors
One of the best ways to communicate information is a visual representation. It is the best way to make the data engaging and understanding. But, you commit certain common blunders while doing so. While using pie charts, you must check the sum of the angles to be always 100%. Also, if the data is large, then don’t go for pie charts. In such cases, using another type of chart will be a better option. Coming on the bar graphs, these are most suitable for the categorical data, but you must consider the following points when examining any bar graph: It shall have a right scale; always try to choose your scale based on the average difference between the numbers given in the data.
2.) Zero Margin of Error
The marginal error is a statistic that tells the amount of random sampling error in a survey data. There is always a room for faults to occur during calculation. Claiming that the marginal error is zero will make other raise their eyebrows as it can only occur in ideal conditions which are seldom. If you nullify this error, you are inviting trouble for yourself.
3.) Botched Numbers
It is a common tendency of humans to believe every data that comes before them. You must check that there are no botched numbers in the samples. To do so, you should ensure that everything adds up to what is given. Always double-check every calculation and get it reviewed by any third person.
4.) Taking Non-Random Samples
If you are going for a survey or research work and you are planning to take non-random samples, then this is the biggest error you can commit. This might kill the purpose of your work. Non-Random samples are often biased, and any study based on such data represents the information of a particular strata of society. It is crucial to ensure that every research or information gathering should be based on the random sample.
5.) Correlation vs. Causation
Most of the students get confused between correlation and causation. Theoretically, the difference between the two can easily be identified. But in daily practice, it remains perplexing for students to establish a difference between cause and effect, and establishing correlation. For those who mostly face this problem, a brief difference is given below:-
Correlation is a number used in statistics that describes the extent & direction of a relationship between two or more variables. If it is positive, then the relationship is directly proportional, and in case of negative correlation, two variables are inversely related.
Causation means that how the occurrence of one event can cause a change in the other, i.e., a causal relationship exists between the two events.
It’s time for you to keep away any ‘oops’ moment by avoiding the mistakes as mentioned above. People often commit errors in statistical calculations, but you can identify them by being a skeptic.
Hope you liked reading this write-up.
If writing lengthy assignments is troubling you, then why add more to your sufferings. Hire us to draft your academic documents. We, at Assignment Desk, have an experienced team of writers and experts who provide the best online Statistics assignment help to scholars at reasonable prices.
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